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Codento’s evaluation thresholds document answers one critical question for every automated check: does a failure stop a release? This distinction between mandatory holds and advisory reports is the foundation of our safety net. We must understand that a reported check measures performance but never blocks deployment, whereas a must-hold check acts as a hard gate. Consequently, this structure allows us to track nuance without sacrificing stability.
Think of a car’s annual inspection to grasp this concept clearly. Brakes and lights are must-hold checks; if they fail, the vehicle cannot legally drive. Fuel efficiency is merely reported; it appears on the sheet but does not prevent the car from passing. Therefore, the thresholds document defines exactly which agent behaviors are brakes and which are fuel consumption metrics.
The five rules governing these decisions form Codento’s methodological core. A check becomes mandatory only after demonstrating consistent success across recent committed runs. Model-judged checks require variance measurement before they can safely hold a gate. This ensures that subjective scoring does not arbitrarily block releases due to minor fluctuations.
Safety rules remain mandatory regardless of dataset size or complexity. For instance, ensuring actions stay within catalog permissions is non-negotiable. When a must-hold check fails, it is either fixed, its bar is adjusted, or it is downgraded with full documentation. This transparency prevents hidden failures from accumulating in the system.
Today, no automatic gate stops a release, though red indicators appear on scorecards. After Nokia’s review, enforcement will begin one agent at a time for Milestone One. The routing evaluation will become a required check for every code change it covers. This phased approach allows for careful validation before full automation takes over.
Let us examine the Coordinator agent’s specific thresholds first. Typed requests reaching the right developer machine must achieve a score of zero point九五. Follow-up turns are reported because they represent a small sample slice where one miss drastically alters the average. Thus, the main routing check absorbs the risk while keeping the metric visible.
FM Triage presents a different profile with sixty-three reported checks out of seventy-nine. Its weak spot is the label-match check, which relies on a judge model with unmeasured variance. Recent tests showed scores fluctuating between zero point七零 and zero点八零. We keep it mandatory because it measures answer quality, but we acknowledge the instability.
The Anomaly Reasoner follows strict rules with exact answers expected. It scored perfectly on seventeen synthetic cases across multiple committed runs. However, development runs prior to fixes were red, highlighting the importance of committed history. Honest reporting includes these details so stakeholders understand the true reliability of the agent.
Action Reasoner enforces safety by restricting actions to allowed catalog rows. A previous failure occurred when an agent guessed an action type, hiding the correct reset. The fix involved searching without a type filter, resulting in immediate green status. The exact-action check remains reported until two more green runs confirm stability.
Operations Hub faces a unique challenge with open defects. Initially, a known defect caused a must-hold check to fail, prompting a proposal to downgrade it. Since the email, the defect was fixed, and the check now passes. If Nokia questions this, offering to keep it mandatory costs little and demonstrates diligence.
The KPI agent reveals significant gaps in recall on real data. Selection recall sits at zero point七五四 against a bar of zero点八. Chat-based answers supported by the catalogue also fail significantly. These failures stem from the judge model being designed for prose, not structured JSON output.
We propose two specific gate changes for Nokia’s consideration. First, demoting the KPI chat check because it measures retired code that no longer exists in production. Second, adjusting the Operations Hub defect check now that the issue is resolved. Both changes are justified by current system state rather than arbitrary rule-breaking.
The most substantive question involves signing off on the KPI gap. The Anomaly Reasoner uses semantic neighbors instead of engineering relationships due to missing data. Signing off means accepting that related KPI context is weak in the October release. This is not a technical failure but a data dependency issue.
Fixes for this gap require KPI relationship data that only Nokia possesses. Formula trees or shared counters would allow Codento to build accurate related-KPI cases. Without this input, the gap persists, and Nokia must consciously accept the limitation. This shifts the burden from code to product knowledge.
Significant changes have occurred since the initial email sent on October ninth. The Coordinator experienced a regression due to an unpinned model version changing behavior unexpectedly. This event proved the value of the thresholds process by catching a drift that no code change caused.
To mitigate future model drift, we recommend pinning model versions per deployment. Treating a model update as a release requiring evaluation adds necessary stability. The nightly routing evaluation currently acts as a drift detector, catching changes within hours. Formalizing this protects against silent degradation.
Other updates include fixing the Operations Hub defect and improving KPI recall. Input screening for instruction injection has been built but lacks real-world testing data. We need Nokia to provide examples of actual injection attempts to validate these checks effectively. Until then, all input screening checks remain reported.
In summary, Codento seeks clear decisions on gate changes and the KPI gap. We need signed acknowledgment of data dependencies and timeline agreements for enforcement. Leaving the meeting with documented answers ensures we can proceed confidently. This collaborative approach builds trust and aligns expectations for the pilot’s success.
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